Class LarsCVScikitsLearnNode

Cross-validated Least Angle Regression model
This node has been automatically generated by wrapping the ``sklearn.linear_model.least_angle.LarsCV`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Read more in the :ref:`User Guide <least_angle_regression>`.
**Parameters**
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
positive : boolean (default=False)
Restrict coefficients to be >= 0. Be aware that you might want to
remove fit_intercept which is set True by default.
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
max_iter: integer, optional
Maximum number of iterations to perform.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
max_n_alphas : integer, optional
The maximum number of points on the path used to compute the
residuals in the cross-validation
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems.
**Attributes**
``coef_`` : array, shape (n_features,)
parameter vector (w in the formulation formula)
``intercept_`` : float
independent term in decision function
``coef_path_`` : array, shape (n_features, n_alphas)
the varying values of the coefficients along the path
``alpha_`` : float
the estimated regularization parameter alpha
``alphas_`` : array, shape (n_alphas,)
the different values of alpha along the path
``cv_alphas_`` : array, shape (n_cv_alphas,)
all the values of alpha along the path for the different folds
``cv_mse_path_`` : array, shape (n_folds, n_cv_alphas)
the mean square error on left-out for each fold along the path
(alpha values given by ``cv_alphas``)
``n_iter_`` : array-like or int
the number of iterations run by Lars with the optimal alpha.
See also
lars_path, LassoLars, LassoLarsCV

Cross-validated Least Angle Regression model
This node has been automatically generated by wrapping the ``sklearn.linear_model.least_angle.LarsCV`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Read more in the :ref:`User Guide <least_angle_regression>`.
**Parameters**
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
positive : boolean (default=False)
Restrict coefficients to be >= 0. Be aware that you might want to
remove fit_intercept which is set True by default.
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
max_iter: integer, optional
Maximum number of iterations to perform.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
max_n_alphas : integer, optional
The maximum number of points on the path used to compute the
residuals in the cross-validation
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems.
**Attributes**
``coef_`` : array, shape (n_features,)
parameter vector (w in the formulation formula)
``intercept_`` : float
independent term in decision function
``coef_path_`` : array, shape (n_features, n_alphas)
the varying values of the coefficients along the path
``alpha_`` : float
the estimated regularization parameter alpha
``alphas_`` : array, shape (n_alphas,)
the different values of alpha along the path
``cv_alphas_`` : array, shape (n_cv_alphas,)
all the values of alpha along the path for the different folds
``cv_mse_path_`` : array, shape (n_folds, n_cv_alphas)
the mean square error on left-out for each fold along the path
(alpha values given by ``cv_alphas``)
``n_iter_`` : array-like or int
the number of iterations run by Lars with the optimal alpha.
See also
lars_path, LassoLars, LassoLarsCV

_stop_training(self,
**kwargs)

execute(self,
x)

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.least_angle.LarsCV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

is_trainable()Static Method

stop_training(self,
**kwargs)

Fit the model using X, y as training data.

This node has been automatically generated by wrapping the sklearn.linear_model.least_angle.LarsCV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.